What it feels like to work in AI right now

At least they’re less obsessed with apes than last summer…

I use ChatGPT to understand these concepts, their background, and to bridge the gap between them.

Getting to the bottom of what Jean Baudrillard really was saying in "Simulacra and Simulation" and applying that to what Larry Wall meant when he said that Perl is the first postmodern language is really something!

Instead, pay attention to how industries and people redefine themselves. There are going to be winners and losers.

It’s the use cases these thing enable that are important.

Today, I wrote a draft product announcement. Only after I was done did I realize I had written it in a really impersonal third person (“users will be able to”). No big deal, but maybe 10-20 minutes of work to make it energetic and second person (“now you can…”).

30 seconds with chatgpt. “Rewrite with more energy, in the second person, using best practices for announcements”).

Six months ago I would never have asked for that. Today it was glorious and let me move on to focus on more important things.

I've used this part of ChatGPT before. Incredibly useful for getting the syntax of some library that you're going to use once in your life, then never again.

Had a sysadmin mate do something similar to generate a simple Chrome plugin for internal use at his work.

That alone justifies the price for ChatGPT Plus, IMO.

LLM have proven to be great as a gimmick or making rather okay localized approximations. You can create a good enough image without any designing skills, or have auto-completion on steroids. However, there is no proof that this same tech can extrapolate to the next level.

Most startups are putting their eggs on this single basket. I have the feeling that this will be what triggers another AI winter and some VCs will holding the bags... but why do I care?

- write and explain me more optimised algorithms for certain cryptographic operations

- explain funny mathy bits of papers that I don’t understand

- plan me a few days of activities for a city holiday

So far it’s been great on all accounts!! I was able to get a faster turnaround time in understanding the papers than I would if I were probing a colleague

- Language learning... The ability to improvise realistic conversations is huge. I can ask to talk about cooking a specific dish or a sport!

- As others have noted, refining documents similar to grammarly

- Looking for a product with extremely specific features (though it isn't very good at comparing yet)

- Searches that are too vague and complicated to articulate to a search engine or use exact matching ...

Need to write a speech as a best man

Write a eulogy for a deceased family member

Recently used it to speed up the time to fix an oauth problem in code I didnt write, knowing nothing about oauth. Replaces stackexchange with much more tailored answers.

can take hundreds of pages of text and distill it down to an executive summary of any length

Daughter uses it to explain how to solve algebra problems, not just give an answer. Will completely change education.

Marketing using it for all content. Devs hate writing blog posts, it will take some code as an input and write a blog post about what it does. Can use it to come up with questions to seed a podcast

Paralegals are virtually redundant. AI with all legal caselaw making it much easier

HR using it to write customized offer letters, review resumes, etc.

It isnt just about asking a question and getting an answer. You can keep adding context and the answers keep getting better.

So I asked it to write me a struct for a table with the "id, name, longitude, latitude, news" columns. That worked well, I was surprised it automatically inferred the data types for said columns.

Then I asked it to write endpoints for retrieving a record from that table and it did so perfectly which again I was surprised by. Asked it to add endpoints for adding records and retrieving all records. Again, no bugs, perfect code. At the end I asked it to create a python script to test the API and it did so flawlessly.

Next day I created a docker env with postgres and went to test the code but it didn't work, turns out it wrote it with mysql in mind so went back and told it to rewrite the entire code with postgres in mind and again it did so flawlessly so overall writing this small API endpoint took maybe 30-60 min.

Considering I was a total newbie at Go this probably would have taken me several hours to complete successfully and this code is basically just boilerplate. I don't care to learn it by heart so I can be more productive in the future. Now that I have ChatGPT I basically don't have to, I don't have to write python to speed up my dev time, I can just have ChatGPT write the basic stuff in a highly performant language. It removed the only drawback which was more boilerplate.

3d and motion and this becomes the holodeck, you describe a scene and it creates it direct to your vr goggles.

Generative AI that can take in a scene and alter it rather than fully create it becomes real augmented reality from sci-fi: not just a HUD or greenscreened in elements, major transformations.

I know three regular people using ChatGPT, here's how they're using it:

1. Franchise consultant: uses it to research opportunities, has it write business letters to franchisees he wishes to contact. Saves him time and is a better writer than he is.

2. Immigration lawyer: uses it to summarize info and write emails to clients. Saves her a ton of time.

3. School teacher: uses it to write report card and assignment feedback. It doesn't save any time at all, but the output is more elegant than if he wrote it manually.

For example, I am studying Japanese and often encounter expressions that seems to mean the same thing; I would ask my teacher, but her time is limited. I can instead ask ChatGPT and only bring to my teacher the questions whose ChatGPT answers did not convince me.

Another example: I like understanding why there are certain steps in a recipe. It would be hard to find someone with the knowledge and time to answer the question, never mind I should pay for their time. ChatGPT can explain what I want to know at the level of detail that I want.

I was also able to get a decent understanding of a mathematical question I had no business understanding by recursively asking questions until I was able to link its answers to my own knowledge.

It was also able to answer questions about the Spring framework that I had while reading the documentation itself. In that context, going in rabbit holes severely slows down learning and has the potential to just get me lost.

What I can ask my computer has just leaped from templated strings to raw human conversation.

I have a hard time imagining something not getting impacted.

First of all iPhone autocomplete.. (but I guess any decade old RNN is an improvement there)

The proper answer is "to be correct."

Thousands of Africans sat at their desks, trying to achieve correctness.

This will become a (correct) commodity, but not today.

(Unless you are really good at differential equations.)

But the jury's still out on the long-run commercial potential of pretty good autocomplete or chat-as-search. It's probably more than zero (like blockchain), but it won't "change everything".

I'd guess that 90% people either agree with your observation or don't notice / care. It's just that these things bring out the vocal defenders, usually a sign people know deep down it's a bit of BS and post out of insecurity

These generative models, whether NLP or vision, are cool but are really examples from a very narrow field. Most ML researchers and practitioners are working in completely different areas and would not obviously benefit from the new generative models (which are themselves prodictized extensions of existing tech trained on more data) so nothing's going to change day-to-day. If you were working on some alternative general purpose generative model, maybe you got scooped. Otherwise it's business as usual.

The "revolution" is happening for tech savvy non-ML people, "tech-bros" colloquially.

I am tempted to write a small program to use to fix my Internet comments and fix my bad English exprimattion , I done some tests and I see that ChatGPT find the places in my comments that can be improved.

You no longer can mock "Please do the needful" from $1 / hr employees from India. They will be communicating at the same level as an average American and the smart ones can completely take over large fields.

A single person, can wear multiple hats and not get blocked.

This can apply at global scale of at least 4 Billion.

Just because you lack imagination doesn't mean it isn't good

I'm going to guess (assume) you probably haven't worked in a 'real' business. A place where elbow grease still does the majority of the work and where Windows 8 was only just phased out.

The killer app (to me) in case of GPT is GPT itself, not ChatGPT. ChatGPT just allows me to easily test use cases for GPT. There are many interesting use cases for those elbow grease businesses for GPT. For example:

Data entry. There's still a lot of data entry being done from unstructured text. Where specifics like names and addresses need to be extracted from letters and emails and contracts and other stuff. I've worked on these challenges before using different strategies and GPT blows my mind with what it can do just by asking to grab this data and format it as JSON. Is it 100% correct? Nope. You still need people to review the data (depending on the use case), but that already saves tons of work.

Categorization. Some companies still get tons of emails that need to be forwarded to specific departments. This is another thing that GPT does surprisingly well out of the box.

And that's just GPT. There are many other legacy business processes that can be automated (partially) by other models that are coming out right now. Even just 'segment anything' that Meta just released is incredibly useful for many use cases I've seen in my daily work.

Killer apps are always a combination of a technology to solve a real world problem. If you don't venture into the real world and only stay part of the tech world, seeing the killer app is very difficult and ends up leading to Juicero-like products.

What I value about these lls is that they are essentially a condensed version of the internet (despite being stupidily large for normal hardware currently)

Usually if I’m building a recommendation or search algorithm I have to use the data from the company I’m working with. This makes it possible for me to encode the entire internet into a model that might be running on a product that only has 100 users.

Funny enough I don’t use it a lot for programming, maybe just to jog my memory on a topic.

One thing you're missing is that we now have a pretty good solution to any NLP pipeline that in the past you'd have to spend months to get right. You can probably still get better results by supervised training on specific tasks but it's good enough. NLP (as we knew it) is dead. This will take some time to show in the applications we use, as people figure out how to use and integrate it, and costs need to come down, but it will make it trivial to add smart functionality for things you previously needed an in-house ML team for.

You can integrate ChatGPT here to help with the proofreading and editing. If you have a list of points, you have have ChatGPT write an email, then integrate its changes. This is useful especially if English is not your first language. ([append]) Here's a quick example. These emails aren't great, but might be better than what I can come up with myself in 2 minutes. https://pastebin.com/dD22gR4y

> I’ve seen “write a snippet of code” demos. But I hardly care about this compared to designing a good API; or designing software that is testable, extensible, maintainable, and follows reasonable design principles.

It's super helpful when starting in a new space. I needed to write a python data munging script the other day. Using a few ChatGPT queries I found dependencies and understood the basics of using them. I still had to check the docs, but I jumped past the "tutorial" and straight to "API reference".

> In fact, no one in my extended sphere of friends and family has asked me anything about chatGPT, midjourney, or any of these other models. The only people I hear about these models from are other tech people.

Counter-anecdote, I met two copywriters working for media publications (non-tech) who both have had GPT-based services integrated into their workflows by the company management.

You’re a better writer than me also English isn’t my first language.

Besides that I used chatgpt to write a eulogy for my father. I know roughly what I wanted to say but I couldn’t find a good way to say it. Chatgpt helped me there even in my own language (Croatian) and there is just no way I could’ve made it as poetic as it did.

A couple of weeks ago they wrote an episode of South Park with ChatGPT (and it was about ChatGPT). It's definitely gone mainstream amongst students who are using it to do homework.

Maybe my favorite was pasting in pages of documentation describing all of the error codes for a library (the docs are the only source of truth) and getting it to output a very good typescript enum.

* Asking about how back-propagation works with multiple output nodes

* Examples of successful ICOs

* A deep dive into what it means to calculate the gradient of a function, with me asking lots of clarifying questions

* A deep dive into how electricity pricing works in the UK, digging into the market clearing price

* Looking for a generic "unwrap one layer of type" utility type in TypeScript

* "In JavaScript, I want to format a date as YYYY-MM-DD_HH-MM-SS"

* Seeing if there's a more concise way to get Zod to define an item that's required, but the value can be defined, other than using a union

* Ideas for naming two user fields, one that's a changed user, one that's the user making the changes

* Digging into the implications that Camilla is called the Queen Consort, not just the Queen

* "Excel I want to show the weekday as a single letter"

So, basically, it's my go-to instead of Google + StackOverflow

Currently you can command kagi/google/http websites to return information. You can infer what should be in Google's search engine and track when information is deleted.

GPT is not commanded, it predicts with inaccuracy. So anybody who wants to black-hole information behind the scenes and never reveal clues to that fact, can do so.

All failed predictions are covered by LLM's design, you cannot infer without serious long term study that something has been removed deliberately. You cannot infer that a valid data entry exists and you failed to retreive it, because unverifiable bs is the default failure state from LLM's.

High level tech people will invest in this, regardless of what the public values in it. Just like Elon's SpaceX and Tesla got lifted out of pitfalls by gov and VC, so too will the AI guys.

Let me put it this way. Hoarde and backup every scrap of online information you care about. Hypothetically an LLM fueled replacement for all of 'the free and open web' websites, could limit information availibity.

A metaphorical example would be leaving out Tianamen Square. Which is fine when you can just Google it, but with the old freedom of information gone, an LLM has the ability to just bs you and you'd never have a reason to infer it existed in the first place.

It's a Super-Injunction by default, a perfect repository for spies to dump data, a librarian who will answer any question but only answer with the truth, if he likes you.

No more Snowden and Assange leaks, there's no way to chase up a deleted video with a search engine.

Anyway you get the idea. In the long run, the structuralists are licking their wet lips at the thought of re-establishing a heirarchy of information access. (Probably, i don't know).

GPT will only ever be a good writing support tool.

People have no clue human intelligence has very little to do with "statistical analysis of old data".

I needed a quick utility window in the Unity editor to see what animations could fire animation events and what those were.

I’m somewhat familiar with the editor API, enough to know what to Google and roughly where to go in the docs. I don’t do it enough though to really learn it beyond that point. So I’d estimate I could spend maybe one and a half hours, counting research, coding something, testing it and then context-switching back to what I was working on.

On a whim I asked ChatGPT (GPT-4) if it could do it for me. Formulating the prompt took a few minutes. I included a short bullet list of what I wanted and told it what Unity version I was on.

In almost an instant, it did it. I copied the code into a new file, added it to my project and it worked.

Time from idea to the first working version was around 10 minutes.

I asked for some minor refinement and then asked how I could extend it. It gave me starting points and taught me something new about Unity. All that slow doc searching, Google searching and forum-trawling was gone.

It’s like having my own personal dev assistant.

Ditto all the redacting work in newspapers, intranet etc. The whole field of proofreaders was virtually extinguished overnight.

Marketing agencies - and I spoke to a few - increased their workers' producitivity 2-4 times (sic!), virtually overnight. Anything from writing briefs to writing copy.

Programming - most of my programming work is deep algorithms, so not much help here, but for writing boiler plate code with new APIs, or writing in a language that I'm a bit rusty in, chatgpt is better than anything else.

Customer service helplines / chatbots (and the same for intranet) - we don't see it just yet, because it takes a bit more time to build a good system, but there are probably thousands projects right now worldwide building those for any niche concievable.

Business intelligence - we used, with success, ChatGPT in our deep tech seedfund, for helping out with initial project ddil.

And, essentially, rubber ducking, but for every single field out there. I just discussed with a psychiatrist how he can use even boilerplate GPT-4 as an additional consultant. You need to be aware of limitations of course, but it is already immensely useful in it's current form - and dedicated solutions for medicine are coming very soon.

That's the short-term perspective and low hanging fruits. On top of that, you have thousands projects now, that are figuring out how to apply LLMs to specific niches. It was difficult before, because you had to train your own models, and now you can just fine tune the existing ones, do embeddings, or just plain prompt engineering.

Oh, and also synergy with different AI modalities - we've had a massive growth in voice recognition and generation, visual recognition, and so on. And LLMs are a glue that adds a layer of understanding underneath.

Art generation is going to be groundbreaking for the advertising industry. Basically, you can hire summer interns from arts academies or liberal arts schools to type cool stuff into Midjourney to generate amazing art for your ads. You don't need to pay for real artists, who, sadly, their original artwork was used to train the model.

The same can be said for low-end graphics arts: Stuff like: Make me a greeting card for this big event. Today, you need to pay a graphic artists to whip something up in 2-72 hours. That will be replace by someone with modest English skills working in a call center somewhere in India or Philippines. They might chat with you for five mins (voice or text chat) to brainstorm ideas. Then, they will "drive" Midjourney and put together a nicely-themed party invite.

On the more advanced side, I do think artists will browse the "best of" portfolios on Midjourney (and others) to get new ideas. They might also use Midjourney to get a head start.

The next logical step beyond Midjourney is to generate the same image, but as a 3D model. I think people (myself included!) really (really, really!) underestimate the cost of creating 3D models for films, adverts, and games. If Midjourney could give you a starting model, you might save hours (or days) of work.

Next-Next: Midjourney can provide basic animations of the same 3D model. Again, you can download in a wide variety of formats, so that you can import and tweak as necessary. Think: Pay to play. Rendered as GIF is cheap as hell, but download advanced CAD format with 50K points in the model might cost 100s of USD. (Remember: You are paying for a SaaS engine, not an expensive, talented artist.) Imagine: "Hey Midjourney, I need a 60 second animation of cute animals, like Animal Crossing, sitting at a table enjoying our new brand of tea called 'It's Great Tea Meet You!'." (Use ChatGPT to generate the first draft of the script.) Writing that just made me think: Ok, now you can add voiceover.

The possibilities for the commercialisation of generating still and moving picture are nearly endless, and many will be useful for the advertising, film, and gaming industry.

Would reddit, or HN still be interesting if there were bots that talked in the popular tone of the subreddit or thread that dominated the conversation?

AI's effect will be like pollution to our culture. It'll speed up and help alot of things and be very helpful generally. But It will create problems in 'human' spaces.

Most impactful is how it destroys the blank page/getting started barrier, second how easy it is to substantially change/adapt/refocus produced work.

It is like having an incredibly efficient, patient and encyclopedic junior collaborator 24/7 at your disposal. It can't be trusted to fully automate without knowledgeable supervision, but it saves a boatload of time and effort.

Q: Describe turning 180 degrees and continuing down stream with the current in a heavily loaded bulk carrier from a stationary position tied to a dock with five knots of downstream current using two 50 ton Ballard pull tractor tugs. Be very detailed and specific in describing the actions required to complete this action safely.

  A: To safely turn a heavily loaded bulk carrier 180 degrees and continue downstream with the current from a stationary position tied to a dock with five knots of downstream current, using two 50-ton bollard pull tractor tugs, you must follow a series of steps. These steps are designed to ensure a smooth and controlled maneuver while minimizing the risk of collision, grounding, or damage to the vessel, dock, and tugs.

  1. Preparation: Prior to beginning the maneuver, ensure that all necessary communication equipment is functional and tested. This includes VHF radios, signal flags, and/or lights. Establish communication protocols and channels with the tugs and any other relevant parties, such as pilots and harbor control. Additionally, check the weather forecast, tidal information, and any local regulations or restrictions that may apply.
 
  2. Positioning of tugs: Position one tug at the bow and the other at the stern of the bulk carrier. The tug at the bow will primarily be responsible for pulling and steering the vessel, while the tug at the stern will act as a brake and assist in maneuvering the vessel as needed. Securely attach the towlines to the designated towing points on the vessel.

  3. Cast off lines: Once the tugs are in position and ready, instruct the crew to release all mooring lines from the dock. As the lines are released, the tugs will need to maintain tension on their towlines to keep the vessel in position and prevent it from drifting downstream with the current.

  4. Initiate the turn: The tug at the bow will start the turning process by pulling the vessel's bow away from the dock and into the current. At the same time, the tug at the stern will push the vessel's stern towards the dock to create a pivoting motion. Both tugs must continuously adjust their power and positioning to maintain control of the vessel as it turns.

  5. Manage the current: During the turn, both tugs must work together to counteract the effects of the five-knot downstream current. The tug at the bow will need to pull harder to overcome the current, while the tug at the stern will need to push and also act as a brake to prevent the vessel from gaining too much speed.

  6. Monitor the turn: Throughout the 180-degree turn, the vessel's crew and tugs must maintain constant communication to ensure a smooth and controlled maneuver. Monitor the vessel's position, speed, and proximity to the dock, other vessels, and any navigational hazards. Adjust the tugs' power and positioning as needed to maintain control and avoid collisions or grounding.

  7. Complete the turn: As the vessel approaches the completion of the 180-degree turn, the tugs will need to adjust their power and positioning to slow down the vessel and align it with the downstream current. The tug at the bow will ease its pulling force, while the tug at the stern will continue pushing until the vessel is properly aligned.

  8. Proceed downstream: Once the vessel is aligned with the current and moving downstream, both tugs will gradually reduce their power to allow the vessel to continue under its own momentum. The tugs will remain close by to provide assistance as needed until the vessel is safely clear of the harbor and any navigational hazards.

  9. Release tugs: When the vessel is at a safe distance from the dock and navigational hazards, and under the control of its own propulsion, the towlines can be released, and the tugs can return to their base.

I have the opposite experience. Everyone I meet outside tech has been exploring ChatGPT, or at least has heard of it and is extremely curious. And in non-tech student circles, a (non STEM) TA I know said students who until a month ago were bad to mediocre (8-12/20 scoring), are suddenly all turning in top 16-18/20 assignments this month. You can argue about what to do given this change, but you can not deny the impact.

* Explaining what a do/while(false) loop was for as I'd not seen that construct before

* Discussing what DIDs were and how web DIDs worked as the RFC was very detailed in a lot of areas I just didn't care about. A discussion with a pretty knowledgeable person in the area was what I needed, and what I got. It explained what the well-known part was, explained my confusion around resolving to a document and resolving to a resource (where I was mentally stuck).

* Creating a learning plan, diagrammed by mermaid, for progressing in bouldering. Each major step broken down into sub-parts to practice

* Finding https://en.wikipedia.org/wiki/Bloom%27s_2_sigma_problem given bad explanations that didn't lead me to the right place in google

* Finding https://blockprotocol.org/ given a shaky memory of "some composable ui framework by someone famous" iterating a little with bing. I had failed to find it before manually.

* Explained and created a table of bouldering gradings as the place I go uses a different one to the videos I see

* Discussed project ideas to do with my son, gave me great ideas around electronics that I think are a good fit as well as a few other things. The most useful part here was being able to say "that's too generic, I need proper projects" and "that's too simple for him" and have it update. It then also created some good explanations at different levels about how radios work,

* General discussions about long term impacts of LLMs, potential use cases

* Career advice

* Generating art that we'll be getting commissioned for the house

* NER without any coding

* Generating ember templates, CSS and example filling data for a custom framework given a problem statement (what I'm actually building right now)

* (edit) I just took requests from my kids and made them some colouring in pages with robots, firefighting robots, lego ninjas, owls, frogs and crabs.

> In fact, no one in my extended sphere of friends and family has asked me anything about chatGPT, midjourney, or any of these other models. The only people I hear about these models from are other tech people.

I've had family members tell me they've used it to create reports, and used it to create marketing copy, a website and lecture slides for others.

Often Im doing something else in the meantime, like coding, in a meeting or having a beer.

Finally we can be drunk and code at the same time ;).

Joking aside, it's been a huge productivity boost, and if you ask things properly will write hugely detailed and correct code.

I've also used to understand other languages/coding I was less familier with, for instance c and sql procedures.

Above only works with v4, 3.5 is to inaccurate. But is indeed slow, small things I can do faster.

With writing articles i've been disappointed so far, even corrections or styles I ask to adjust it puts in back in a few questions later.

However writing children stories for helping my kid learn how to read it's really good, in every language. "Write a story for kids of age ... about ... use the following words". Came up with nearly perfect stories my kid loved.

From me and around me:

- marketing asked me to show them the ropes of mid-journey yesterday, boss said "this will be the face of our new product" to one of my hasty "creations".

- mom learns english with chatGTP because she finished duolingo

- I wrote a PoC demo for a prompt engineering tool and gpt4 demo chat in a day. That would have taken me days without gpt4. (material design, storing in local storage, gobbling up the data specification, save everything on change not with buttons)

- First draft for some diagrams in mermaid js worked well too, or converting from flow diagram to a swimlane diagram.

- All kind of personal data cleaning: dirty list of emails > ready to paste in mail client, wall of text with broken new line characters > sub-headlines and paragraphs

- virtual assistants (e.g. company chat-/voicebots), needs a bit of tooling but gpt4 is totally ready for it (apart from latency and price)

- 30 minutes to a browser-add-on that marks tweets as "seen" so I can skip them if I scrolled past them before. (userscript to be precise)

- understanding tax regulations %)

What I'm waiting for:

- better knowledge ingestion so it can use my notes

- personalization over time

- good dev-ops integration (push and deploy for me too).

- maybe something to have better separation of concerns on code so the messiness matters less, not sure if possible :)

- remote control my screen

- running an LLM locally

- have it build its own plugins for any website or service

Spam of all kinds and at automated-industry scale. Imagine blog-spam written in a variety of styles so you can no longer easily identify it as such. Imagine chum-boxes that no longer repeat themselves and are harder to identify as such. Imagine ads masquerading as content, as it already exists, but at scale.

And every time you slip up and click on one, it will learn a little more about you and create chum content better tailored to you.

Generative AI will facilitate a flood of algorithmic spam.

This kind of spam already exists, it just isn't scaled because it still takes time and resources to create and the people creating it are not the brightest, so for now it is easy to identify.

There's insane potential to automate it. Think b2b, not b2c.

But soon people will just enter one goal, "make me money" and the program will go on a loop, pausing only when making an important decision to get the approval of the owner

Also, the input buffer of LLMs is increasing. Soon we will be able to begin with "write me a full 3D game"

I can fire up an AI to build out my missing unit tests in 10 minutes what would take a developer 3 weeks to accomplish.

Scale that for 155 different projects … that’s over a year of development time in about 24 hours of compute

All of our government funded researchers who worked on natural language processing can now throw their work in the trash and resign. ChatGPT is leagues better than anything that they've done. And OpenAI weren't even trying.

Windows wasn't even localised in Latvian properly until very recently. Google translate still spits out ridiculous translations. (Though it's better than before). Most software isn't even available in Latvian. Almost no video games are in Latvian. Only the most popular books and movies are being translated. Interested in something less popular - you better learn other languages.

And now ChatGPT comes out and I can ask it to write C++ functions in Latvian. I don't need to learn English to be a programmer any more. Nothing like that has been done before. It translates stuff way better than google. And it will only get better.

Imagine that there's a book that only 50 people from Latvia are interested in. Human translators aren't going to bother. But ChatGPT can do it easily.

This is a very big deal.

Rule 34. When in doubt, always defer to rule 34.

Sure, it can help you write an email now, but the real magic could be when these things come together in a symphony of intelligence.

So... I don't know where you're working. But don't twiddle your thumbs for too long! It's no fun to be in the last half of people to leave the sinking ship.

If you think GPT is just about chat, you've misunderstood LLMs.

Top comment: I love seeing my job get transformed from 3D artist into prompt writer into jobless in a year or less, yay!

Working on the practical side of ML/AI at FAANG, you will probably be working with some combination of feature stores, training platforms, inference engines, and so on - all attempting to optimize inference and models for specific use cases - largely ranking - which ads to show which customers based on feature store attributes, which shows to show which customers - all these ranking problems exist orthogonal to ChatGPT, which is using relatively stale datasets to answer knowledge based questions.

The scaling problems for AI/ML for productionizing these ranking models from training to inference is a huge scaling problem. ChatGPT hasn't really come close to solving it in a general way (and also solves a different class of problems).

It's really not that complicated. Gatekeeping is so over.

I would call that “applied AI” and there’s no shame in figuring out novel ways to apply a new technology.

I have a robot vacuum cleaner with "AI" computer vision, and you can just tell that it doesn't see the world in the way a living being does. It'll detect and avoid shoes, cables and dog turds, but is completely blind to anything not in its specific library of objects (we have a two year old, it chokes all the time on things like ribbons and toys).

Being able to recognise objects that cause it to choke, even if it doesn't what they're called in English, is souch more significant to robotics than being able to label things.

Already have use cases for that too!

Incredible. To be clear, your group and the company you work for saw fit to release your beta-quality autopilot onto public roads, as it was hitting human-looking objects in your test labs. Is that what you're admitting here? Did anyone in your group object to this? Were you personally concerned?

Instead of moving fast and breaking things, what if you had not rushed your autopilot out, but waited on technology to improve to the point that your product wouldn't hit a human-looking object it had never seen before?

This has been a pathology in the computer/software industry for a very long time. It's never been actually true except in a couple of special cases, but the industry acts as if it is. That has led to all sorts of bizarre and undesirable things.

> Guys, what the heck are we doing??

I think we're playing with fire and, without extreme caution and careful consideration (which I'm not seeing much of), this could end very, very badly for both the industry and society.

I have always been optimistic about technology and society, but (perhaps like you), my optimism has largely evaporated over the last several weeks. I wish the future didn't look so dark. Perhaps, though, things will look less gloomy with time.

In tech world the cost for R&D/design/code is generally much larger then continuous code. That means that it's extremely expensive to have 1 user, but really cheap to have 1M users.

Thus winner-takes-all makes sense, as company who is ahead can continue developing/designing at a cheaper price as opposed to company with no users.

- Customers were only willing to walk so far to go to a bakery

- A store could only stock so many varieties on it's shelves

Now, the cost of distributing goods across the globe, and the incremental cost of creating new goods has shrunk to virtually zero for digital goods, combined with people's general hesitance to the friction of switching providers, creates a the natural "winner take all" effect

The places where you'll see this NOT happening are areas where those costs are still significant for some reason.

Examples:

- Different ride sharing services are popular in different countries, many of them local ones, since foreign companies had a harder time getting distribution started.

- Legal regulations also have been hindering the spread of payment companies across country-level borders

Basically, with tech, you can build a product with a very limited amount of people and resources with a potentially infinite customer base. And this means that this product can suck up money from the whole world and redirect it into the hands of very few. At its essence, this is the case. With tech, this phenomena is exacerbated to the extreme compared to other industries where more physical resources and labor are required to scale production and distribution of the product, which means more wealth getting spread.

At its core, tech has the potential to exacerbate wealth inequality in mind-boggling proportions.

Our current economic system is not made for tech.

"To those who have everything, more will be given, and from those who have nothing, everything will be taken."

However, fortunately, a much more optimal solution happened, where somebody build a superior and cheap solution (unfortunately not open yet) and let the whole world leverage it. Now your friend needs to waste very little energy deduplicating, and presumably has free time and energy to think about other problems that aren't solved.

It's hard to imagine, but I wonder if there's some non-parallelizable machine learning algorithms which might outperform these massive models? It seems improbable, but it's a small hope I've had. The greatest intellects were aware of (ourselves) do not scale very well, and maybe the same will ultimately apply to AI?

Welcome to how the entire world probably feels, but way worse because they haven't been getting 6-7 figure salaries for the last ten years to insulate themselves for what big tech wants to see happen.

I think it's a positive thing that AI researchers are feeling this way because it might be the only thing that slows things down or at least shows us a little more empathy.

I'm not counting on it, but yeah, all I can say is, get used to it. I'm basically just leaning on trying to enjoy things that aren't in front of me, kind of like being more mindful while we're in free fall, while simultaneous fretting and praying for future generations and hoping they have a future.

An existential double whammy; going back and forth between feeling you're smart enough to compete, if only you had the capital, and that the computers are smarter and better at everything.

Millions of people are going to have problems putting food on the table and providing a roof over their head, if the progress of these AIs doesn't halt immediately, or if some form of UBI isn't enacted.

I’d say the differentiator is that in addition to hype scammers, you have people like Stephen Wolfram excited.

I wonder what the next "get rich quick" techbro fad will be.

Not everything that catches fire is destined to leave nothing but ashes in its wake.

Many of us have already learned stuff / achieved stuff / built stuff with the help of ChatGPT and the OpenAI API that would have taken us MONTHs to achieve.

It's the inverse because crypto was tools and business models in search of a problem, while LLMs are problem solvers in search of a business model.

Building LLM products is fun but... any day you could be wrecked by an OpenAI update.

Then why is Bard so bad? Bard feels like GPT-2 or LLaMA 7B with no finetuning most of the time (I tried it two or three times over the course of a week and went back to ChatGPT)

Yes, the release was wild and disruptive, but when your core motivation isn't greed, you can do some seriously wild and disruptive things.

The issue with Language models for the companies that create them is they are so general. If a company builds a language model into their backend, and another one comes out from a different company that's 1.3x as good, it would be trivial to switch to the better one. It's not like a company being so tied to AWS that they can't even fathom switching to another cloud platform because all their internal shit is built in to the thousand specific ways AWS works. As a result, to be competitive you need to be the very best in that realm.

https://askyourpdf.com/

I’m paying the $10 a month and I hate subscriptions.

It is the most useful tool I have ever used as a programmer. It's absolutely not a "hype train".

I’m sure there’s a good deal of over-promising, overexuberant salesmen trying to make a quick buck from it too a la crypto, but the technology is absolutely useful and providing real value as-is.

…unlocks phone using face and then asks it to translate something to French…

ChatGPT was released November 30th 2022. How's ML research hiring looking over the past four months?

ChatGPT is good at a lot of things, but searching the web is not one of them. That's why bing uses it for the natural language and contextual conversation aspects, supplementing it with traditional web search.